Spelling mistakes are common, and most people are used to software indicating if a mistake was made. From autocorrect on our phones, to red underlining in text editors, spell checking is an essential feature for many different products.
The first program to implement spell checking was written in 1971
The constant growth of data on the Internet creates a demand for a tool that could process textual information in a faster way with no effort from the ordinary user.
Moreover, it's highly important that this instrument of text analysis could implement solutions for both low and high-level NLP
This tutorial is an introduction to a simple optimization technique called gradient descent, which has seen major application in state-of-the-art machine learning models.
We'll develop a general purpose routine to implement gradient descent and apply it to solve different problems, including classification via supervised learning.
In this process, we'll
State-of-the-art technologies in NLP allow us to analyze natural languages on different layers: from simple segmentation of textual information to more sophisticated methods of sentiment categorizations.
However, it does not inevitably mean that you should be highly advanced in programming to implement high-level tasks such as sentiment analysis in
The amount of textual data on the Internet has significantly increased in the past decades. There's no doubt that the processing of this amount of information must be automated, and the TextBlob package is one of the fairly simple ways to perform NLP - Natural Language Processing.
It provides
Facial detection is a powerful and common use-case of Machine Learning. It can be used to automatize manual tasks such as school attendance and law enforcement. In the other hand, it can be used for biometric authorization.
In this article, we'll perform facial detection in Python, using OpenCV.
This article is an introduction to kernel density estimation using Python's machine learning library scikit-learn.
Kernel density estimation (KDE) is a non-parametric method for estimating the probability density function of a given random variable. It is also referred to by its traditional name, the Parzen-Rosenblatt Window method, after its
Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications.
In many of these applications, deep learning
Understand your data better with visualizations! With over 330+ pages, you'll learn the ins and outs of visualizing data in Python with popular libraries like Matplotlib, Seaborn, Bokeh, and more.
Includes a free 30 page Seaborn guide!
